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(In a past job interview I failed at explaining how to calculate and interprete ROC curves – so here goes my attempt to fill this knowledge gap.) Think of a regression model mapping a number of features onto a real number (potentially a probability). The resulting real number can then be mapped on one of two classes, depending on whether this predicted number is greater or lower than some choosable threshold. Let’s take for example a logistic regression and data on the survivorship of the Titanic accident to introduce the relevant concepts which will lead naturally to the ROC (Receiver Operating Characteristic) and its AUC or AUROC (Area Under ROC Curve).

Caffe is an open-source deep learning framework originally created by Yangqing Jia which allows you to leverage your GPU for training neural networks. As opposed to other deep learning frameworks like Theano or Torch you don’t have to program the algorithms yourself; instead you specify your network by means of configuration files. Obviously this approach is less time consuming than programming everything on your own, but it also forces you to stay within the boundaries of the framework, of course. Practically though this won’t matter most of the time as the framework Caffe provides is quite powerful and continuously advanced.

… or Inferring Identity from Observations

Let’s assume the following application:

A conservation organisation starts a project to geographically catalogue the remaining representatives of an endangered plant species. For that purpose hikers are encouraged to communicate the location of the plant if they encounter it. Due to those hikers using GPS technology ranging from cheap smartphones to highend GPS devices and weather as well as environmental circumstances the measurements are of varying accuracy. The goal of the conservation organisation is to build up a map locating all found plants with an ID assigned to them. Now every time a new location measurement is entered into the system a clustering is applied to identify related measurements – i.e. belonging to the same plant.

For the previous article I needed a quick way to figure out if two sets of points are linearly separable. But for crying out loud I could not find a simple and efficient implementation for this task. Except for the perceptron and SVM – both are sub-optimal when you just want to test for linear separability. The perceptron is guaranteed to finish off with a happy ending – if feasible – but it can take quite a while. And SVMs are designed for soft-margin classification which means that they might settle for a not separating plane and also they maximize the margin around the separating plane – which is wasted computational effort.

I am excited to announce that this is supposed to be my first article published also on r-bloggers.com :)

The processing of data needs to take dimensionality into account as usual metrics change their behaviour in subtle ways, which impacts the efficiency of algorithms and methods that are based on distances / similarities of data points. This has been tagged the “curse of dimensionality“. Just as well, in some cases high dimensionality can aid us when investigating data – “blessing of dimensionality”. But in general it is, as usual, a good thing to know what’s going on and so let’s have a look at what dimensionality does to data.

The Titanic challenge on Kaggle is about inferring from a number of personal details whether a passenger survived the disaster or did not. I gave two algorithms a try, which are decision trees using R package party and SVMs using R package kernlab. I chose to use party for the decision trees over the more prominent rpart because the authors of party make a very good point why their approach is likely to outperform it and other approaches in terms of generalization.

This article is about the “Digit Recognizer” challenge on Kaggle. You are provided with two data sets. One for training: consisting of 42’000 labeled pixel vectors and one for the final benchmark: consisting of 28’000 vectors while labels are not known. The vectors are of length 784 (28×28 matrix) with values from 0 to 255 (to be interpreted as gray values) and are supposed to be classified as to what number (from 0 to 9) it represents. The classification is realized using SVMs which I implement with kernlab package in R.

Three representation of the data set

The pre- and post-processing in all cases consists of removing the unused (white – pixel value = 0) frame (rows and columns) of every matrix and finally to scale the computed feature vectors (feature-wise) to mean 0 and standard deviation 1. I gave three representations a try:

To deepen my knowledge about Machine Learning I decided last year to attend “Learning From Data” on edX. This online course was designed by Yaser Abu-Mostafa – a renowned expert on the subject and professor of Electrical Engineering and Computer Science at California Institute of Technology (Caltech). I can say without the slightest hesitation that this course was a wonderful intellectual experience. Prof. Abu-Mostafa conceived the course so skilfully that it was as much a joy to attend, as it was challenging. And this finding couldn’t be further from a naturalness, especially given that the syllabus took a path through quite theoretical terrain.